
# Initial imports
import pandas as pd
import hvplot.pandas
from path import Path
import plotly.express as px
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans


# Load data
file_path = "Resources/crypto_data.csv"
crypto_df = pd.read_csv(file_path, index_col=[0])
crypto_df.head(10)


# Find null values
for column in crypto_df.columns:
    print(f"Column {column} has {crypto_df[column].isnull().sum()} null values")

Column CoinName has 0 null values
Column Algorithm has 0 null values
Column IsTrading has 0 null values
Column ProofType has 0 null values
Column TotalCoinsMined has 508 null values
Column TotalCoinSupply has 0 null values


# Keep all the cryptocurrencies that are being traded.
crypto_df = crypto_df.loc[crypto_df['IsTrading'] == True]
print(crypto_df.shape)
crypto_df.head(10)

(1144, 6)


# Keep all the cryptocurrencies that have a working algorithm.
crypto_df = crypto_df.dropna(subset = ["Algorithm"])
print(crypto_df.shape)
crypto_df.head(10)

(1144, 6)


# Remove the "IsTrading" column. 
crypto_df = crypto_df.drop(['IsTrading'], axis=1)
print(crypto_df.shape)
crypto_df.head(10)

(1144, 5)


# Remove rows that have at least 1 null value.
crypto_df = crypto_df.dropna()
print(crypto_df.shape)
crypto_df.head(10)

(685, 5)


# Keep the rows where coins are mined.
crypto_df = crypto_df.loc[crypto_df['TotalCoinsMined']> 0]
print(crypto_df.shape)
crypto_df.head(10)

(532, 5)


# Create a new DataFrame that holds only the cryptocurrencies names.
cryptoname_df = crypto_df.filter(['CoinName'], axis=1)
print(crypto_df.shape)
cryptoname_df.head(5)

(532, 5)


# Drop the 'CoinName' column since it's not going to be used on the clustering algorithm.
crypto_df = crypto_df.drop(['CoinName'], axis=1)
print(crypto_df.shape)
crypto_df.head(10)

(532, 4)


# Use get_dummies() to create variables for text features.
crypto1_df = pd.get_dummies(crypto_df, columns=['Algorithm', 'ProofType'])
print(crypto1_df.shape)
crypto1_df.head(10)

(532, 98)


# Standardize the data with StandardScaler().
scaler = StandardScaler()
# Fitting the Standard Scaler with the training data.
crypto_scaled = scaler.fit_transform(crypto1_df)
print(crypto_scaled[0:5])

[[-0.11710817 -0.1528703  -0.0433963  -0.0433963  -0.0433963  -0.06142951
  -0.07530656 -0.0433963  -0.06142951 -0.06142951 -0.0433963  -0.0433963
  -0.19245009 -0.06142951 -0.09740465 -0.0433963  -0.11547005 -0.07530656
  -0.0433963  -0.0433963  -0.15191091 -0.0433963  -0.13118084 -0.0433963
  -0.0433963  -0.08703883 -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.06142951 -0.0433963  -0.08703883 -0.08703883 -0.08703883 -0.0433963
  -0.13118084 -0.13840913 -0.13840913 -0.0433963  -0.06142951 -0.0433963
  -0.07530656 -0.18168574 -0.0433963  -0.0433963  -0.0433963  -0.07530656
  -0.15826614 -0.31491833 -0.0433963  -0.08703883 -0.07530656 -0.06142951
   1.38675049 -0.0433963  -0.0433963  -0.06142951 -0.0433963  -0.0433963
  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.39879994 -0.0433963  -0.18168574 -0.0433963  -0.08703883 -0.08703883
  -0.10680283 -0.0433963  -0.13118084 -0.0433963  -0.0433963  -0.0433963
  -0.0433963  -0.07530656 -0.43911856 -0.0433963  -0.06142951 -0.0433963
  -0.0433963  -0.89632016 -0.0433963  -0.0433963   1.42222617 -0.0433963
  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.0433963  -0.0433963 ]
 [-0.09396955 -0.145009   -0.0433963  -0.0433963  -0.0433963  -0.06142951
  -0.07530656 -0.0433963  -0.06142951 -0.06142951 -0.0433963  -0.0433963
  -0.19245009 -0.06142951 -0.09740465 -0.0433963  -0.11547005 -0.07530656
  -0.0433963  -0.0433963  -0.15191091 -0.0433963  -0.13118084 -0.0433963
  -0.0433963  -0.08703883 -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.06142951 -0.0433963  -0.08703883 -0.08703883 -0.08703883 -0.0433963
  -0.13118084 -0.13840913 -0.13840913 -0.0433963  -0.06142951 -0.0433963
  -0.07530656 -0.18168574 -0.0433963  -0.0433963  -0.0433963  -0.07530656
  -0.15826614 -0.31491833 -0.0433963  -0.08703883 -0.07530656 -0.06142951
   1.38675049 -0.0433963  -0.0433963  -0.06142951 -0.0433963  -0.0433963
  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.39879994 -0.0433963  -0.18168574 -0.0433963  -0.08703883 -0.08703883
  -0.10680283 -0.0433963  -0.13118084 -0.0433963  -0.0433963  -0.0433963
  -0.0433963  -0.07530656 -0.43911856 -0.0433963  -0.06142951 -0.0433963
  -0.0433963  -0.89632016 -0.0433963  -0.0433963   1.42222617 -0.0433963
  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.0433963  -0.0433963 ]
 [ 0.52494561  4.48942416 -0.0433963  -0.0433963  -0.0433963  -0.06142951
  -0.07530656 -0.0433963  -0.06142951 -0.06142951 -0.0433963  -0.0433963
  -0.19245009 -0.06142951 -0.09740465 -0.0433963  -0.11547005 -0.07530656
  -0.0433963  -0.0433963  -0.15191091 -0.0433963  -0.13118084 -0.0433963
  -0.0433963  -0.08703883 -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.06142951 -0.0433963  -0.08703883 -0.08703883 -0.08703883 -0.0433963
  -0.13118084 -0.13840913 -0.13840913 -0.0433963  -0.06142951 -0.0433963
  -0.07530656 -0.18168574 -0.0433963  -0.0433963  -0.0433963  -0.07530656
  -0.15826614 -0.31491833 -0.0433963  -0.08703883 -0.07530656 -0.06142951
  -0.72111026 -0.0433963  -0.0433963  -0.06142951 -0.0433963  -0.0433963
  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.39879994 -0.0433963   5.50400923 -0.0433963  -0.08703883 -0.08703883
  -0.10680283 -0.0433963  -0.13118084 -0.0433963  -0.0433963  -0.0433963
  -0.0433963  -0.07530656 -0.43911856 -0.0433963  -0.06142951 -0.0433963
  -0.0433963  -0.89632016 -0.0433963  -0.0433963   1.42222617 -0.0433963
  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.0433963  -0.0433963 ]
 [-0.11671506 -0.15255998 -0.0433963  -0.0433963  -0.0433963  -0.06142951
  -0.07530656 -0.0433963  -0.06142951 -0.06142951 -0.0433963  -0.0433963
  -0.19245009 -0.06142951 -0.09740465 -0.0433963  -0.11547005 -0.07530656
  -0.0433963  -0.0433963  -0.15191091 -0.0433963  -0.13118084 -0.0433963
  -0.0433963  -0.08703883 -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.06142951 -0.0433963  -0.08703883 -0.08703883 -0.08703883 -0.0433963
  -0.13118084 -0.13840913 -0.13840913 -0.0433963  -0.06142951 -0.0433963
  -0.07530656 -0.18168574 -0.0433963  -0.0433963  -0.0433963  -0.07530656
  -0.15826614  3.17542648 -0.0433963  -0.08703883 -0.07530656 -0.06142951
  -0.72111026 -0.0433963  -0.0433963  -0.06142951 -0.0433963  -0.0433963
  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.39879994 -0.0433963  -0.18168574 -0.0433963  -0.08703883 -0.08703883
  -0.10680283 -0.0433963  -0.13118084 -0.0433963  -0.0433963  -0.0433963
  -0.0433963  -0.07530656 -0.43911856 -0.0433963  -0.06142951 -0.0433963
  -0.0433963   1.11567277 -0.0433963  -0.0433963  -0.70312305 -0.0433963
  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.0433963  -0.0433963 ]
 [-0.11474682 -0.1528703  -0.0433963  -0.0433963  -0.0433963  -0.06142951
  -0.07530656 -0.0433963  -0.06142951 -0.06142951 -0.0433963  -0.0433963
  -0.19245009 -0.06142951 -0.09740465 -0.0433963  -0.11547005 -0.07530656
  -0.0433963  -0.0433963  -0.15191091 -0.0433963   7.62306442 -0.0433963
  -0.0433963  -0.08703883 -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.06142951 -0.0433963  -0.08703883 -0.08703883 -0.08703883 -0.0433963
  -0.13118084 -0.13840913 -0.13840913 -0.0433963  -0.06142951 -0.0433963
  -0.07530656 -0.18168574 -0.0433963  -0.0433963  -0.0433963  -0.07530656
  -0.15826614 -0.31491833 -0.0433963  -0.08703883 -0.07530656 -0.06142951
  -0.72111026 -0.0433963  -0.0433963  -0.06142951 -0.0433963  -0.0433963
  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.39879994 -0.0433963  -0.18168574 -0.0433963  -0.08703883 -0.08703883
  -0.10680283 -0.0433963  -0.13118084 -0.0433963  -0.0433963  -0.0433963
  -0.0433963  -0.07530656 -0.43911856 -0.0433963  -0.06142951 -0.0433963
  -0.0433963   1.11567277 -0.0433963  -0.0433963  -0.70312305 -0.0433963
  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963  -0.0433963
  -0.0433963  -0.0433963 ]]


# Using PCA to reduce dimension to three principal components.
pca = PCA(n_components=3)
crypto_pca = pca.fit_transform(crypto_scaled)
crypto_pca

array([[-0.33453058,  1.02245356, -0.49560737],
       [-0.3178626 ,  1.02274894, -0.49595228],
       [ 2.30618856,  1.67632477, -0.60190593],
       ...,
       [ 0.32223237, -2.29307998,  0.36221211],
       [-0.13880578, -1.96702498,  0.3980826 ],
       [-0.28659074,  0.84775993, -0.25370339]])


# Create a DataFrame with the three principal components.
pcs_df = pd.DataFrame(data=crypto_pca, columns=['PC 1', 'PC 2', 'PC 3'])
pcs_df.index = cryptoname_df.index
print(pcs_df.shape)
pcs_df.head(10)

(532, 3)


# Create an elbow curve to find the best value for K.
inertia = []
k = list(range(1, 11))
# Calculate the inertia for the range of K values
for i in k:
   km = KMeans(n_clusters=i, random_state=0)
   km.fit(pcs_df)
   inertia.append(km.inertia_)

D:\ProgramData\Anaconda3\envs\mlenv\lib\site-packages\sklearn\cluster\_kmeans.py:882: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.
  f"KMeans is known to have a memory leak on Windows "


elbow_data = {"k": k, "inertia": inertia}
df_elbow = pd.DataFrame(elbow_data)
df_elbow.hvplot.line(x="k", y="inertia", title="Elbow Curve", xticks=k)


# Initialize the K-Means model.
model = KMeans(n_clusters=4, random_state=0)

# Fit the model
model.fit(pcs_df)

# Predict clusters
predictions = model.predict(pcs_df)
predictions

array([0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1,
       1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,
       1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1,
       1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0,
       0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,
       0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,
       1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0,
       0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0,
       0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1,
       0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1,
       1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,
       1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,
       0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,
       1, 1, 1, 0, 3, 0, 1, 1, 1, 0, 3, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
       1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,
       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,
       1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 3, 1, 0, 1, 1, 0, 0,
       1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1,
       0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0,
       0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 3, 0, 2, 0, 0,
       0, 1, 1, 0])


# Create a new DataFrame including predicted clusters and cryptocurrencies features.
# Concatentate the crypto_df and pcs_df DataFrames on the same columns.
clustered_df = crypto_df.join(pcs_df)

#  Add a new column, "CoinName" to the clustered_df DataFrame that holds the names of the cryptocurrencies. 
# YOUR CODE HERE
clustered_df = clustered_df.join(cryptoname_df)

#  Add a new column, "Class" to the clustered_df DataFrame that holds the predictions.
clustered_df["class"] = model.labels_

# Print the shape of the clustered_df
print(clustered_df.shape)
clustered_df.head(10)

(532, 9)


# Creating a 3D-Scatter with the PCA data and the clusters
fig = px.scatter_3d(
    clustered_df,
    x="PC 1",
    y="PC 2",
    z="PC 3",
    color="class",
    symbol="class",
    width=800,
    hover_name="CoinName",
    hover_data=["Algorithm"]
)
fig.update_layout(legend=dict(x=0, y=1))
fig.show()


# Create a table with tradable cryptocurrencies.
clustered_df.hvplot.table(columns=["CoinName", "Algorithm", "ProofType", "TotalCoinSupply", "TotalCoinsMined", "class"])


# Print the total number of tradable cryptocurrencies.
print(f'Total Number of Tradable Currencies:',clustered_df['CoinName'].count())

Total Number of Tradable Currencies: 532


# Scaling data to create the scatter plot with tradable cryptocurrencies.
X = clustered_df[['TotalCoinSupply', 'TotalCoinsMined']]
X_scaled = MinMaxScaler().fit_transform(X)
X_scaled

array([[4.20000000e-11, 0.00000000e+00],
       [5.32000000e-04, 1.06585544e-03],
       [3.14159265e-01, 2.95755135e-02],
       ...,
       [1.40022261e-03, 9.90135079e-04],
       [2.10000000e-05, 7.37028150e-06],
       [1.00000000e-06, 1.29582282e-07]])


# Create a new DataFrame that has the scaled data with the clustered_df DataFrame index.
X_df = pd.DataFrame(data=X_scaled, columns=["TotalCoinSupply", "TotalCoinsMined"], index=clustered_df.index)

# Add the "CoinName" column from the clustered_df DataFrame to the new DataFrame.
X_df = X_df.join(clustered_df['CoinName'])

# Add the "Class" column from the clustered_df DataFrame to the new DataFrame. 
X_df = X_df.join(clustered_df['class'])
print(X_df.shape)
X_df.head(10)

(532, 4)


# Create a hvplot.scatter plot using x="TotalCoinsMined" and y="TotalCoinSupply".
X_df.hvplot.scatter(
    x="TotalCoinSupply",
    y="TotalCoinsMined",
    hover_cols=["CoinName","class"],
    by="class",
)

	CoinName	Algorithm	IsTrading	ProofType	TotalCoinsMined	TotalCoinSupply
42	42 Coin	Scrypt	True	PoW/PoS	4.199995e+01	42
365	365Coin	X11	True	PoW/PoS	NaN	2300000000
404	404Coin	Scrypt	True	PoW/PoS	1.055185e+09	532000000
611	SixEleven	SHA-256	True	PoW	NaN	611000
808	808	SHA-256	True	PoW/PoS	0.000000e+00	0
1337	EliteCoin	X13	True	PoW/PoS	2.927942e+10	314159265359
2015	2015 coin	X11	True	PoW/PoS	NaN	0
BTC	Bitcoin	SHA-256	True	PoW	1.792718e+07	21000000
ETH	Ethereum	Ethash	True	PoW	1.076842e+08	0
LTC	Litecoin	Scrypt	True	PoW	6.303924e+07	84000000

	CoinName	Algorithm	IsTrading	ProofType	TotalCoinsMined	TotalCoinSupply
42	42 Coin	Scrypt	True	PoW/PoS	4.199995e+01	42
365	365Coin	X11	True	PoW/PoS	NaN	2300000000
404	404Coin	Scrypt	True	PoW/PoS	1.055185e+09	532000000
611	SixEleven	SHA-256	True	PoW	NaN	611000
808	808	SHA-256	True	PoW/PoS	0.000000e+00	0
1337	EliteCoin	X13	True	PoW/PoS	2.927942e+10	314159265359
2015	2015 coin	X11	True	PoW/PoS	NaN	0
BTC	Bitcoin	SHA-256	True	PoW	1.792718e+07	21000000
ETH	Ethereum	Ethash	True	PoW	1.076842e+08	0
LTC	Litecoin	Scrypt	True	PoW	6.303924e+07	84000000

	CoinName	Algorithm	IsTrading	ProofType	TotalCoinsMined	TotalCoinSupply
42	42 Coin	Scrypt	True	PoW/PoS	4.199995e+01	42
365	365Coin	X11	True	PoW/PoS	NaN	2300000000
404	404Coin	Scrypt	True	PoW/PoS	1.055185e+09	532000000
611	SixEleven	SHA-256	True	PoW	NaN	611000
808	808	SHA-256	True	PoW/PoS	0.000000e+00	0
1337	EliteCoin	X13	True	PoW/PoS	2.927942e+10	314159265359
2015	2015 coin	X11	True	PoW/PoS	NaN	0
BTC	Bitcoin	SHA-256	True	PoW	1.792718e+07	21000000
ETH	Ethereum	Ethash	True	PoW	1.076842e+08	0
LTC	Litecoin	Scrypt	True	PoW	6.303924e+07	84000000

	CoinName	Algorithm	ProofType	TotalCoinsMined	TotalCoinSupply
42	42 Coin	Scrypt	PoW/PoS	4.199995e+01	42
365	365Coin	X11	PoW/PoS	NaN	2300000000
404	404Coin	Scrypt	PoW/PoS	1.055185e+09	532000000
611	SixEleven	SHA-256	PoW	NaN	611000
808	808	SHA-256	PoW/PoS	0.000000e+00	0
1337	EliteCoin	X13	PoW/PoS	2.927942e+10	314159265359
2015	2015 coin	X11	PoW/PoS	NaN	0
BTC	Bitcoin	SHA-256	PoW	1.792718e+07	21000000
ETH	Ethereum	Ethash	PoW	1.076842e+08	0
LTC	Litecoin	Scrypt	PoW	6.303924e+07	84000000

	CoinName	Algorithm	ProofType	TotalCoinsMined	TotalCoinSupply
42	42 Coin	Scrypt	PoW/PoS	4.199995e+01	42
404	404Coin	Scrypt	PoW/PoS	1.055185e+09	532000000
808	808	SHA-256	PoW/PoS	0.000000e+00	0
1337	EliteCoin	X13	PoW/PoS	2.927942e+10	314159265359
BTC	Bitcoin	SHA-256	PoW	1.792718e+07	21000000
ETH	Ethereum	Ethash	PoW	1.076842e+08	0
LTC	Litecoin	Scrypt	PoW	6.303924e+07	84000000
DASH	Dash	X11	PoW/PoS	9.031294e+06	22000000
XMR	Monero	CryptoNight-V7	PoW	1.720114e+07	0
ETC	Ethereum Classic	Ethash	PoW	1.133597e+08	210000000

Clustering Crypto¶

Deliverable 1: Preprocessing the Data for PCA¶

Deliverable 2: Reducing Data Dimensions Using PCA¶

Deliverable 3: Clustering Crytocurrencies Using K-Means¶

Finding the Best Value for `k` Using the Elbow Curve¶

Deliverable 4: Visualizing Cryptocurrencies Results¶

3D-Scatter with Clusters¶

	PC 1	PC 2	PC 3
42	-0.334531	1.022454	-0.495607
404	-0.317863	1.022749	-0.495952
1337	2.306189	1.676325	-0.601906
BTC	-0.145326	-1.353450	0.182259
ETH	-0.155134	-2.013085	0.371618
LTC	-0.164206	-1.101826	-0.003118
DASH	-0.398211	1.157685	-0.418778
XMR	-0.155237	-2.206105	0.389927
ETC	-0.153575	-2.013163	0.371596
ZEC	-0.138805	-1.967025	0.398083

	TotalCoinSupply	TotalCoinsMined	CoinName	class
42	4.200000e-11	0.000000	42 Coin	0
404	5.320000e-04	0.001066	404Coin	0
1337	3.141593e-01	0.029576	EliteCoin	0
BTC	2.100000e-05	0.000018	Bitcoin	1
ETH	0.000000e+00	0.000109	Ethereum	1
LTC	8.400000e-05	0.000064	Litecoin	1
DASH	2.200000e-05	0.000009	Dash	0
XMR	0.000000e+00	0.000017	Monero	1
ETC	2.100000e-04	0.000115	Ethereum Classic	1
ZEC	2.100000e-05	0.000007	ZCash	1

	TotalCoinsMined	TotalCoinSupply	...	ProofType_PoW/PoS
42	4.199995e+01	42	...	1
404	1.055185e+09	532000000	...	1
1337	2.927942e+10	314159265359	...	1
BTC	1.792718e+07	21000000	...	0
ETH	1.076842e+08	0	...	0
LTC	6.303924e+07	84000000	...	0
DASH	9.031294e+06	22000000	...	1
XMR	1.720114e+07	0	...	0
ETC	1.133597e+08	210000000	...	0
ZEC	7.383056e+06	21000000	...	0

Clustering Crypto¶

Deliverable 1: Preprocessing the Data for PCA¶

Deliverable 2: Reducing Data Dimensions Using PCA¶

Deliverable 3: Clustering Crytocurrencies Using K-Means¶

Finding the Best Value for k Using the Elbow Curve¶

Deliverable 4: Visualizing Cryptocurrencies Results¶

3D-Scatter with Clusters¶

Finding the Best Value for `k` Using the Elbow Curve¶